【数字图像分析】基于Python实现 Canny Edge Detection（Canny 边缘检测算法）

Canny 边缘检测算法

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Steps:

1. 高斯滤波平滑
2. 计算梯度大小和方向
3. 非极大值抑制
4. 双阈值检测和连接

代码结构：

Canny Edge Detection
|	Gaussian_Smoothing
|		|	convolution.py
|		|	|	convolution()
|		|	gaussion_smoothing.py
|		|	|	dnorm()
|		|	|	gaussian_kernel()
|		|	|	gaussian_blur()
|	Sobel_Filter
|		|	sobel.py
|		|	|	sobel_edge_detection()
|	Canny.py
|		|	non_max_suppression()
|		|	threshold()
|		|	hysteresis()
|		|	main()

代码解读：

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1. 高斯滤波平滑

• 创建一个高斯核(kernel_size=5)：

  

• 执行卷积和平均操作（以下均以 lenna 图为例）

  

2. 计算梯度大小和方向

水平方向和竖直方向

梯度图：

3. 非极大值抑制

4. 双阈值检测和连接

---

以下是代码：

import numpy as np
import cv2
import argparse

from Computer_Vision.Canny_Edge_Detection.sobel import sobel_edge_detection
from Computer_Vision.Canny_Edge_Detection.gaussian_smoothing import gaussian_blur

import matplotlib.pyplot as plt

def non_max_suppression(gradient_magnitude, gradient_direction, verbose):
    image_row, image_col = gradient_magnitude.shape

    output = np.zeros(gradient_magnitude.shape)

    PI = 180

    for row in range(1, image_row - 1):
        for col in range(1, image_col - 1):
            direction = gradient_direction[row, col]

            if (0 <= direction < PI / 8) or (15 * PI / 8 <= direction <= 2 * PI):
                before_pixel = gradient_magnitude[row, col - 1]
                after_pixel = gradient_magnitude[row, col + 1]

            elif (PI / 8 <= direction < 3 * PI / 8) or (9 * PI / 8 <= direction < 11 * PI / 8):
                before_pixel = gradient_magnitude[row + 1, col - 1]
                after_pixel = gradient_magnitude[row - 1, col + 1]

            elif (3 * PI / 8 <= direction < 5 * PI / 8) or (11 * PI / 8 <= direction < 13 * PI / 8):
                before_pixel = gradient_magnitude[row - 1, col]
                after_pixel = gradient_magnitude[row + 1, col]

            else:
                before_pixel = gradient_magnitude[row - 1, col - 1]
                after_pixel = gradient_magnitude[row + 1, col + 1]

            if gradient_magnitude[row, col] >= before_pixel and gradient_magnitude[row, col] >= after_pixel:
                output[row, col] = gradient_magnitude[row, col]

    if verbose:
        plt.imshow(output, cmap='gray')
        plt.title("Non Max Suppression")
        plt.show()

    return output

def threshold(image, low, high, weak, verbose=False):
    output = np.zeros(image.shape)

    strong = 255

    strong_row, strong_col = np.where(image >= high)
    weak_row, weak_col = np.where((image <= high) & (image >= low))

    output[strong_row, strong_col] = strong
    output[weak_row, weak_col] = weak

    if verbose:
        plt.imshow(output, cmap='gray')
        plt.title("threshold")
        plt.show()

    return output

def hysteresis(image, weak):
    image_row, image_col = image.shape

    top_to_bottom = image.copy()

    for row in range(1, image_row):
        for col in range(1, image_col):
            if top_to_bottom[row, col] == weak:
                if top_to_bottom[row, col + 1] == 255 or top_to_bottom[row, col - 1] == 255 or top_to_bottom[row - 1, col] == 255 or top_to_bottom[
                    row + 1, col] == 255 or top_to_bottom[
                    row - 1, col - 1] == 255 or top_to_bottom[row + 1, col - 1] == 255 or top_to_bottom[row - 1, col + 1] == 255 or top_to_bottom[
                    row + 1, col + 1] == 255:
                    top_to_bottom[row, col] = 255
                else:
                    top_to_bottom[row, col] = 0

    bottom_to_top = image.copy()

    for row in range(image_row - 1, 0, -1):
        for col in range(image_col - 1, 0, -1):
            if bottom_to_top[row, col] == weak:
                if bottom_to_top[row, col + 1] == 255 or bottom_to_top[row, col - 1] == 255 or bottom_to_top[row - 1, col] == 255 or bottom_to_top[
                    row + 1, col] == 255 or bottom_to_top[
                    row - 1, col - 1] == 255 or bottom_to_top[row + 1, col - 1] == 255 or bottom_to_top[row - 1, col + 1] == 255 or bottom_to_top[
                    row + 1, col + 1] == 255:
                    bottom_to_top[row, col] = 255
                else:
                    bottom_to_top[row, col] = 0

    right_to_left = image.copy()

    for row in range(1, image_row):
        for col in range(image_col - 1, 0, -1):
            if right_to_left[row, col] == weak:
                if right_to_left[row, col + 1] == 255 or right_to_left[row, col - 1] == 255 or right_to_left[row - 1, col] == 255 or right_to_left[
                    row + 1, col] == 255 or right_to_left[
                    row - 1, col - 1] == 255 or right_to_left[row + 1, col - 1] == 255 or right_to_left[row - 1, col + 1] == 255 or right_to_left[
                    row + 1, col + 1] == 255:
                    right_to_left[row, col] = 255
                else:
                    right_to_left[row, col] = 0

    left_to_right = image.copy()

    for row in range(image_row - 1, 0, -1):
        for col in range(1, image_col):
            if left_to_right[row, col] == weak:
                if left_to_right[row, col + 1] == 255 or left_to_right[row, col - 1] == 255 or left_to_right[row - 1, col] == 255 or left_to_right[
                    row + 1, col] == 255 or left_to_right[
                    row - 1, col - 1] == 255 or left_to_right[row + 1, col - 1] == 255 or left_to_right[row - 1, col + 1] == 255 or left_to_right[
                    row + 1, col + 1] == 255:
                    left_to_right[row, col] = 255
                else:
                    left_to_right[row, col] = 0

    final_image = top_to_bottom + bottom_to_top + right_to_left + left_to_right

    final_image[final_image > 255] = 255

    return final_image

if __name__ == '__main__':
    ap = argparse.ArgumentParser()
    ap.add_argument("-i", "--image", required=True, help="Path to the image")
    ap.add_argument("-v", "--verbose", type=bool, default=False, help="Path to the image")
    args = vars(ap.parse_args())

    image = cv2.imread(args["image"])

    blurred_image = gaussian_blur(image, kernel_size=9, verbose=False)

    edge_filter = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])

    gradient_magnitude, gradient_direction = sobel_edge_detection(blurred_image, edge_filter, convert_to_degree=True, verbose=args["verbose"])

    new_image = non_max_suppression(gradient_magnitude, gradient_direction, verbose=args["verbose"])

    weak = 50

    new_image = threshold(new_image, 5, 20, weak=weak, verbose=args["verbose"])

    new_image = hysteresis(new_image, weak)

    plt.imshow(new_image, cmap='gray')
    plt.title("Canny Edge Detector")
    plt.show()

References

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